A deep learning-based intelligent decision-making model for tumor and cancer cell identification

Putta Durga, Deepthi Godavarthi

Abstract


In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.

Keywords


Cancer cells; Deep learning; Intelligent decision-making approach; Tumors; VGG-19

Full Text:

PDF


DOI: https://doi.org/10.11591/eei.v13i1.6469

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).